Drug repositioning: a machine-learning approach through data integration

• Published in: Journal of cheminformatics Vol. 5; no. 1; p. 30
• Main Authors: Napolitano, Francesco, Zhao, Yan, Moreira, Vânia M, Tagliaferri, Roberto, Kere, Juha, D’Amato, Mauro, Greco, Dario
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 22.06.2013; BioMed Central Ltd; Springer Nature B.V; BioMed Central
• Subjects: Algorithms; Chemistry; Chemistry and Materials Science; Classification; Colleges & universities; Computational Biology/Bioinformatics; Computer Applications in Chemistry; Data mining; Disease; Documentation and Information in Chemistry; Drugs; Gene expression; Genes; Health aspects; Machine learning; Methods; Pharmaceuticals; Protein-protein interactions; Proteins; Research Article; Theoretical and Computational Chemistry; ATC code; CMap; Antineoplastic; Drug repositioning; Anthelmintics; Niclosamide; SMILES; SVM; Integrative genomics; Mode of action; Machine learning; Oxamniquine; Connectivity map
• ISSN: 1758-2946; 1758-2946
• DOI: 10.1186/1758-2946-5-30

Existing computational methods for drug repositioning either rely only on the gene expression response of cell lines after treatment, or on drug-to-disease relationships, merging several information levels. However, the noisy nature of the gene expression and the scarcity of genomic data for many diseases are important limitations to such approaches. Here we focused on a drug-centered approach by predicting the therapeutic class of FDA-approved compounds, not considering data concerning the diseases. We propose a novel computational approach to predict drug repositioning based on state-of-the-art machine-learning algorithms. We have integrated multiple layers of information: i) on the distances of the drugs based on how similar are their chemical structures, ii) on how close are their targets within the protein-protein interaction network, and iii) on how correlated are the gene expression patterns after treatment. Our classifier reaches high accuracy levels (78%), allowing us to re-interpret the top misclassifications as re-classifications, after rigorous statistical evaluation. Efficient drug repurposing has the potential to significantly impact the whole field of drug development. The results presented here can significantly accelerate the translation into the clinics of known compounds for novel therapeutic uses.